Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/plaidml/plaidml

PlaidML is a framework for making deep learning work everywhere.
https://github.com/plaidml/plaidml

Last synced: 22 days ago
JSON representation

PlaidML is a framework for making deep learning work everywhere.

Awesome Lists containing this project

README 

        






*A platform for making deep learning work everywhere.*






[![License]](https://github.com/plaidml/plaidml/blob/master/LICENSE)

[![Build status](https://badge.buildkite.com/87cb87799399a2e27c6f99b1839a66e9101b6f132b46d36089.svg)](https://buildkite.com/intel/tpp-plaidml)



# To Our Users



First off, we’d like to thank you for choosing PlaidML. Whether you’re a new

user or a multi-year veteran, we greatly appreciate you for the time you’ve

spent tinkering around with our source code, sending us feedback, and improving

our codebase. PlaidML would truly not be the same without you.



The feedback we have received from our users indicates an ever-increasing need

for performance, programmability, and portability.  During the past few months,

we have been restructuring PlaidML to address those needs. Below is a summary of

the biggest changes: 

* We’ve adopted [MLIR], an extensible compiler infrastructure that has gained

  industry-wide adoption since its release in early 2019. MLIR makes it easier

  to integrate new software and hardware into our compiler stack, as well as

  making it easier to write optimizations for our compiler.

* We’ve worked extensively on [Stripe], our low-level intermediate

  representation within PlaidML. Stripe contains optimizations that greatly

  improve the performance of our compiler. While our work on Stripe began before

  we decided to use MLIR, we are in the process of fully integrating Stripe into

  MLIR.

* We created our C++/Python embedded domain-specific language ([EDSL])

  to improve the programmability of PlaidML.



Today, we’re announcing a new branch of PlaidML — `plaidml-v1`. This will act as

our development branch going forward and will allow us to more rapidly prototype

the changes we’re making without breaking our existing user base. As a

precaution, please note that certain features, tests, and hardware targets may

be broken in `plaidml-v1` as is a research project. Right now `plaidml-v1`

only supports Intel and AMD CPUs with AVX2 and AVX512 support.



You can continue to use code on the `master` branch or from our releases on

PyPI. For your convenience, the contents of our `master` branch will be released

as version 0.7.0. There is no further development in this branch. `plaidml-v1` is 

a research project.



-----



PlaidML is an advanced and portable tensor compiler for enabling deep learning

on laptops, embedded devices, or other devices where the available computing

hardware is not well supported or the available software stack contains

unpalatable license restrictions.



PlaidML sits underneath common machine learning frameworks, enabling users to

access any hardware supported by PlaidML. PlaidML supports [Keras], [ONNX], and

[nGraph].



As a component within the [nGraph Compiler stack], PlaidML further extends the

capabilities of specialized deep-learning hardware (especially GPUs,) and makes

it both easier and faster to access or make use of subgraph-level optimizations

that would otherwise be bounded by the compute limitations of the device.



As a component under [Keras], PlaidML can accelerate training workloads with

customized or automatically-generated Tile code. It works especially well on

GPUs, and it doesn't require use of CUDA/cuDNN on Nvidia hardware, while

achieving comparable performance.



PlaidML works on all major operating systems: Linux, macOS, and Windows.



## Building PlaidML from source 



Due to use of conda PlaidML runs on all major Linux distributions.



```

export PLAIDML_WORKSPACE_DIR=[choose a directory of your choice]



# setting up miniconda env

cd ${PLAIDML_WORKSPACE_DIR}

wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.12.0-Linux-x86_64.sh

bash Miniconda3-py37_4.12.0-Linux-x86_64.sh -p ${PLAIDML_WORKSPACE_DIR}/miniconda3

eval "$(${PLAIDML_WORKSPACE_DIR}/miniconda3/bin/conda shell.bash hook)"

conda activate



# clone plaidml-v1 and set up env

git clone https://github.com/plaidml/plaidml.git --recursive -b plaidml-v1

cd plaidml

conda env create -f environment.yml -p .cenv/

conda activate .cenv/



# we might need to go into .cenv/bin and create a sym-link 

cd .cenv/bin/

ln -s ninja ninja-build

cd ../../



# preparing PlaidML build

./configure



# buidling PlaidML

cd build-x86_64/Release

ninja && PYTHONPATH=$PWD python plaidml/plaidml_setup.py

```



## Demos and Related Projects



### Plaidbench



[Plaidbench] is a performance testing suite designed to help users compare the

performance of different cards and different frameworks.



```

cd build-x86_64/Release

ninja plaidbench_py && PYTHONPATH=$PWD KMP_AFFINITY=granularity=fine,verbose,compact,1,0 OMP_NUM_THREADS=8 python plaidbench/plaidbench.py -n128 keras resnet50

```



The command above is suited for 8-core Intel/AMD CPUs with hyper-threading enabled. E.g. on an Intel i9-11900K we expect around 8.5ms latency. 



## Reporting Issues



Either open a ticket on [GitHub].



## CI & Validation



### Validated Hardware



A comprehensive set of tests for each release are run against the hardware

targets listed below.



* AMD CPUs with AVX2 and AVX512

* Intel CPUs with AVX2 and AVX512



### Validated Networks



We support all of the Keras application networks from

current versions of 2.x. Validated networks are tested for performance and

correctness as part of our continuous integration system.



* CNNs

  * Inception v3

  * ResNet50

  * VGG19

  * VGG16

  * Xception

  * DenseNet



[LIBXSMM]: https://github.com/libxsmm/libxsmm/ 

[nGraph Compiler stack]: https://ngraph.nervanasys.com/docs/latest/

[Keras]: https://keras.io/

[GitHub]: https://github.com/plaidml/plaidml/issues

[ONNX]: https://github.com/onnx

[nGraph]: https://github.com/NervanaSystems/ngraph

[License]: https://img.shields.io/badge/License-Apache%202.0-blue.svg

[Build status]: https://badge.buildkite.com/5c9add6b89a14fd498e69a5035062368480e688c4c74cbfab3.svg?branch=master

[Plaidbench]: https://github.com/plaidml/plaidml/tree/plaidml-v1/plaidbench

[EDSL]: https://plaidml.github.io/plaidml/docs/edsl

[MLIR]: https://mlir.llvm.org/

[Stripe]: https://arxiv.org/abs/1903.06498